A company must train a model to classify images of various animal species. It already has a large set of labeled images and will not label additional data. What learning approach should the company use?

Supervised learning is the correct approach because the company has a large dataset of labeled images. In supervised learning, the model learns from input-output pairs (images and their corresponding animal species labels) to map new inputs to correct outputs. Unsupervised learning is used when data is unlabeled and the goal is to find hidden patterns or structures. Reinforcement learning involves an agent learning through trial and error with rewards and penalties, which is not applicable here. Active learning is a specialized form of supervised learning where the algorithm interactively queries a user to label new data points, which is explicitly ruled out by the problem statement ("will not label additional data").